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ST-Raptor: LLM-Powered Semi-Structured Table Question Answering

Created by
  • Haebom

Author

Zirui Tang, Boyu Niu, Xuanhe Zhou, Boxiu Li, Wei Zhou, Jiannan Wang, Guoliang Li, Xinyi Zhang, Fan Wu

Outline

This paper proposes ST-Raptor, a novel framework for automating query answering of semi-structured tables, widely used in real-world applications. Semi-structured tables have complex layouts, such as hierarchical headers and merged cells, making accurate query answering difficult using existing NL2SQL, NL2Code, and multi-modal LLM QA methods. ST-Raptor uses hierarchical orthogonal trees (HO-trees) to represent complex layouts and enables LLM query processing through basic tree operations. It decomposes user queries into subqueries, generates a tree operation pipeline, and performs operation-table alignment to ensure accurate pipeline execution. Furthermore, forward and backward validation are used to enhance the accuracy of the results. We evaluate the performance of our approach using a new dataset, SSTQA, consisting of 102 real semi-structured tables and 764 questions, achieving up to 20% higher accuracy than existing methods.

Takeaways, Limitations

Takeaways:
We present an effective approach to the semi-structured table query-answering problem.
We propose a framework based on the HO-Tree model and tree operations that effectively handle complex table layouts.
Improves the reliability of responses through a two-step verification mechanism.
We provide a new benchmark dataset, SSTQA.
It shows up to 20% improved accuracy compared to existing methods.
Limitations:
The size of the SSTQA dataset may be relatively small.
The HO-Tree model may not perfectly capture all types of semi-structured table layouts.
It is dependent on the performance of LLM, and the limitations of LLM may affect the performance of ST-Raptor.
Generalization performance for different types of questions needs to be further validated.
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